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Creators/Authors contains: "Gerritz, E"

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  1. ; ; (, https://openreview.net/forum?id=orYMrUv7eu)
    Generalization to unseen data is a key desideratum for deep networks, but its re- lation to classification accuracy is unclear. Using a minimalist vision dataset and a measure of generalizability, we show that popular networks, from deep con- volutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures. Accuracy is not a good predictor of generalizability, and generalization varies non-monotonically with layer depth. Our code is available at github.com/dyballa/generalization 
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    Accepted Manuscript Full Text Available